Contextualizing Lessons Learned from Sharing Knowledge-Building Relationships: Aboriginal Experiences in the Cross-Cultural Workplace
Bibliographic record
Abstract
Questioning, seeking information and understanding, and ultimately learning, always occurs within a context, and that context affects what questions are asked, how information and understanding are sought, and ultimately, what learning occurs. Knowing the place, the time, and the people involved in any quest for understanding is to know more about how the learning took place and how new understandings were developed. The larger context for the lessons from research that was facilitated through the Sharing Knowledge-Building Relationships: Aboriginal Experiences in the Cross-Cultural Workplace gathering reported elsewhere in this volume (Adair, Kwantes, Stonefish, Badea, & Weir, this volume) is important to consider as it shaped the questions, methods and learning. Personal experiences and societal context prompt questions, inform seeing, and impact understanding. This article, therefore, seeks to set the context for the information shared at this 2 day gathering with a focus on Aboriginal experiences in the workplace, setting the stage for understanding the time and the place for the learning that took place, by explicating the societal context, the location, and the activities of this event.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.032 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".